Development of quantitative MRI DTI analysis tool for preterm neonate

早产儿定量MRI DTI分析工具的开发

基本信息

批准号：
8510698
负责人：
Kenichi Oishi
金额：
$ 43.72万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-09-20 至 2016-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8510698
关键词：
Age Anatomy Atlases Birth Brain Brain Mapping Caring Child Childhood Cognitive deficits Databases Deformity Development Diagnosis Diagnostic Diffusion Magnetic Resonance Imaging Early Intervention Gestational Age Goals Image Image Analysis Impairment Infant Intervention Knowledge Lesion Life Live Birth Magnetic Resonance Imaging Manuals Maps Measures Medical Mental disorders Methods Metric Modality Neonatal Neonatal Intensive Care Neurocognitive Deficit Neurologic Neurological outcome Normal Range Outcome Patients Population Pregnancy Premature Birth Premature Infant Process Prognostic Marker Reporting Research Signal Transduction Staging Structure Survival Rate Symptoms Technology Term Birth Testing Time United States Weight base brain volume clinically relevant functional outcomes gray matter imaging modality improved ischemic lesion neonate nervous system disorder neuropsychological outcome forecast premature research clinical testing tool white matter

项目摘要

DESCRIPTION (provided by applicant): We will establish a quantification method for neonatal brain MRI to evaluate the abnormalities of preterm born neonates. In the U.S., approximately 12% of all neonates are born preterm (<37 weeks gestation), with 2% of these being very preterm (VPB, 28-32 weeks gestation). The percentage of preterm births has been increasing over the last ten years, partly due to improved neonatal care for preterm infants. Nevertheless, about half of the VPB infants may develop clinically-evident neurological or psychological disorders and the number could be even higher if subtle functional abnormalities are included. The extent of neurocognitive deficits in the late preterm infants (33-36 weeks gestation) is also unknown. However, most of the neuro- cognitive deficits are not easily detected during the first year of life. To benefit from early intervention and to develop more deficits-specific interventions, we need methods to detect and quantify brain abnormalities at an early stage. MRI is one of the most promising and sensitive methods to detect subtle anatomic abnormalities in the neonatal brain. Previous brain MRI studies have found some correlations between several types of abnormalities and neurological outcomes, but there are also reports that found no relationship between signal alterations and neurological outcomes. Hence, the current knowledge does not justify the use of MRI for routine clinical evaluations. To optimize the usefulness of MRI for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is needed. The proposed method is based on two core technologies for the quantification of neonatal brain anatomy: a deformable brain atlas with detailed anatomic information and a highly elastic topology-preserved warping method. The combination will provide multiple MR parameters from 176 automatically segmented brain structures. The goals of this project are to establish an atlas-based, automated quantification method for the neonatal brain, to evaluate the detail anatomy of premature neonates at a term-equivalent age. The overall hypothesis is that our DTI-guided quantitative brain analysis will sensitively detect anatomical abnormalities of preterm born neonates in region- specific manner. We have four specific aims. In Aim 1, we will create a multi-contrast (T1-, T2-weighted, and DTI) normal-term neonatal brain atlas for quantitative brain analysis, which will be a statistical representation of the population ("Bayesian atlas"). In Aim 2, we will combine the Bayesian atlas with highly elastic topology- preserved warping (Large Deformation Diffeomorphic Metric Mapping, LDDMM) for automated brain segmentation and test the segmentation accuracy. In Aim 3, we will use the combination of the Bayesian atlas and LDDMM to perform T1/T2/DTI quantification of term neonatal brain MRIs. In Aim 4, we will apply the method to the brain MRIs from term-equivalent preterm born infants (born at 28-36 weeks gestational age) and compare the MR parameters to those in the term infants. This study will be a first step toward seeking very early prognostic indicators for functional outcomes of the anatomical brain abnormalities in preterm births.

描述（申请人提供）：我们将建立新生儿脑部MRI的量化方法来评估早产新生儿的异常情况。在美国，大约 12% 的新生儿早产（妊娠 <37 周），其中 2% 为极早产（VPB，妊娠 28-32 周）。过去十年来，早产儿的比例一直在增加，部分原因是早产儿新生儿护理的改善。然而，大约一半的 VPB 婴儿可能会出现临床上明显的神经或心理疾病，如果包括细微的功能异常，这个数字可能会更高。晚期早产儿（妊娠 33-36 周）神经认知缺陷的程度也是未知的。然而，大多数神经认知缺陷在生命的第一年不容易被发现。为了从早期干预中受益并开发更多针对缺陷的干预措施，我们需要在早期阶段检测和量化大脑异常的方法。 MRI 是检测新生儿大脑细微解剖异常最有前途和最灵敏的方法之一。先前的脑部 MRI 研究发现几种类型的异常与神经系统结果之间存在某些相关性，但也有报告发现信号改变与神经系统结果之间没有关系。因此，目前的知识并不能证明使用 MRI 进行常规临床评估是合理的。为了优化 MRI 在新生儿和儿科护理中的实用性，需要基于定量图像分析和功能相关性的系统研究。该方法基于新生儿大脑解剖学量化的两项核心技术：具有详细解剖信息的可变形大脑图谱和高弹性拓扑保留的扭曲方法。该组合将提供来自 176 个自动分割的大脑结构的多个 MR 参数。该项目的目标是建立一种基于图谱的新生儿大脑自动量化方法，以评估足月相当年龄的早产儿的详细解剖结构。总体假设是，我们的 DTI 引导的定量大脑分析将以特定区域的方式灵敏地检测早产新生儿的解剖异常。我们有四个具体目标。在目标 1 中，我们将创建一个多对比度（T1、T2 加权和 DTI）正常足月新生儿脑图谱用于定量脑分析，这将是群体的统计表示（“贝叶斯图谱”）。在目标 2 中，我们将贝叶斯图集与高弹性拓扑保留扭曲（大变形微分同胚度量映射，LDDMM）相结合，用于自动大脑分割并测试分割精度。在目标 3 中，我们将结合使用贝叶斯图集和 LDDMM 对足月新生儿脑部 MRI 进行 T1/T2/DTI 量化。在目标 4 中，我们将将该方法应用于足月当量早产儿（胎龄 28-36 周出生）的脑部 MRI，并将 MR 参数与足月儿的参数进行比较。这项研究将是寻找早产儿大脑解剖异常功能结果的早期预后指标的第一步。